The invention relates to a method and system for processing data relating to the preformance of apparatus having a number of distinct components in order to estimate both changes both changes in the performance of the individual components making up the apparatus and biases or systematic errors in a measurment system being used being used to determine the performance of the apparatus and its components. Although it has particular relevance to analyzing the performance of gas turbines, the present invention has more general applications to a wide variety of apparatus including machines, particularly prime movers and other rotary machines, plants and such as chemical process plants, generating and distribution plants, etc.
Monitoring the performance of such machines and plants plant can yield important data on maintenance requirements to achieve safe, economical and reliable operation. It is particularly important in such circumstances to determine both which components of the machine or plant being monitored have deviated from their required performance specifications and which measurements being used for monitoring have developed systematic errors, in order to refurbish or replace only those components which have deteriorated and refurbish, replace or recalibrate faulty measuring equipment that, because of the presence of systematic errors, is producing spurious indications.
It is important to note that the overall performance of any apparatus made up of a number of components is completely determined by the performance of such components. For example, in a gas turbine, overall performance measurements such as fuel consumption, speed, exhaust temperature, etc. are determined by component performance parameters such as efficiency, flow capacity, etc. Generally, changes in one or more component performance parameters will cause changes in one of more overall performance measurements, and it is only possible to alter overall performance measurements by altering one or more component performance parameters. Thus, it is only possible to improve the fuel consumption of a gas turbine by altering the efficiency end/or flow capacity of one or more of its components. Naturally, some apparent changes in overall performance may be caused, not by component performance changes, but by biases in the measurements used to determine the overall performance. The problem exists, therefore, to be able to assess, from an analysis of changes in performance measurements, both the component preformance parameters that have changed and biases in the measurements.
To assist the engineer in this task, recent years have seen the introduction of computer-based performance data analysis systems, particularly in the field of gas turbines. From the preceding comments it can be appreciated that any analysis has to be performed using a number of known data items (i.e. performance measurements) which are generally less than the number of unknown data items (i.e. component performance parameter changes and sensor biases). There is therefore no single true solution set of component performance parameter changes and sensor biases to be derived from a particular set of performance measurement data, and techniques are required that derive the most likely solution. In particular, so-called Optimal Estimation or Kalman Filtering computation techniques have been used to analyse gas turbine performance data (see, for example, "Gas Path Analysis applied to Turbine Engine Condition Monitoring", L. A. Urban, AIAA 72-1082 (1973) and "Gas Path Analysis: An Approach to Engine Diagnostics", Dr A. J. Volponi, 35th Mechanical Failure Prevention Group (1982)). The basic theory behind Kalman Filtering is also well known (see, for example, "Digital and Kalman Filtering", S. M. Bozic, Pub E Arnold (1984); "Applied Optimal Estimation", A Gelb (ed), Pub MIT Press (1974); "Applied Optimal Control", A. E. Bryson and Y. C. Ho, Pub Halstead Press (1975); "System Identification", P. Eykhoff, Pub Wiley (1974)).
The Kalman Filter is an algorithm for producing, from given data, the most likely solution, based on the method of least squares. Stated in its simplest form it may be seen as a weighting matrix (sometimes termed the Kalman Gain Matrix) which inter-relates a priori information (specifically a covariance matrix of component changes and sensor biases, a measurement repeatability covariance matrix and a System Matrix, i.e. a matrix giving the relationships between observed measurement changes and component changes and sensor biases) to enable the most likely set of component changes and sensor biases to be from the observed performance measurement changes.
The Kalman Filter algorithm is thus given a set of observed performance measurement changes. Each of these changes represents the change in value of a parameter from a datum level (at which there are no component changes or sensor biases to be taken into account) to an operational level (which has been affected by such changes and biases). The algorithm calculates which set of component performance changes and sensor biases is most likely to have caused the given set of observed performance measurement changes, using information described in the preceding paragraph.
An inherent shortcoming of the Kalman Filter is that, because it operates on the basis of least squares, it tends to allocate a value to all possible component changes and sensor biases, even if the observed performance measurement changes input to the filter are due to only a small sub-set of the possible component changes and sensor biases. Thus, the effects of any genuine component performance changes and/or sensor biases tend to be "smeared" over all possible component changes and sensor biases, leading the an under-estimation of actual component changes and/or sensor biases which may have occurred in any particular situation.
In the extensive literature describing the use of the Kalman Filter for the analysis of apparatus (particularly gas turbines), this problem is either ignored or addressed in one of four different ways. First the problem may be alleviated, but not eliminated, by adjusting the a priori matrices used to calculate the Kalman Gain Matrix. Second, additional algorithms which assume the presence of a single component change or sensor bias are run after after the Kalman Filter. These algorithms are activated if the results from the Kalman Filter exceed certain thresholds. Third, searches are made for sensor biases using separate algorithms run before and/or after the Kalman Filters, Fourth, "banks" of Kalman Filters are set up to analyze a given piece of apparatus. Each filter in such a bank is set up to analyze a different combination of component changes and sensor biases, while other algorithms are used to decide which filter is correct in any given sitution.
These approaches are unsatisfactory because they either work within the restrictions of the Kalman Filter without solving the basic "smearing" tendency, or rely on gross assumptions about the number, combinations, and magnitudes of component changes and/or sensor biases, which may not always be correct.